{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[3] Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch normalization: Forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:  [ 27.18502186  34.21455511  37.68611762]\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  mean:  [  5.32907052e-17   7.04991621e-17   4.11476409e-17]\n",
      "  std:  [ 0.99999999  1.          1.        ]\n",
      "After batch normalization (nontrivial gamma, beta)\n",
      "  means:  [ 11.  12.  13.]\n",
      "  stds:  [ 0.99999999  1.99999999  2.99999999]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization\n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print('  means: ', a.mean(axis=0))\n",
    "print('  stds: ', a.std(axis=0))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'})\n",
    "print('  mean: ', a_norm.mean(axis=0))\n",
    "print('  std: ', a_norm.std(axis=0))\n",
    "\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print('After batch normalization (nontrivial gamma, beta)')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:  [ 1.01531428  1.01238373  0.97819988]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch Normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.70292611676e-09\n",
      "dgamma error:  7.42041421625e-13\n",
      "dbeta error:  2.87950576558e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch Normalization: alternative backward (OPTIONAL, +3 points extra credit)\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For the sigmoid function, it turns out that you can derive a very simple formula for the backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can also derive a simple expression for the batch normalization backward pass if you work out derivatives on paper and simplify. After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster.\n",
    "\n",
    "NOTE: This part of the assignment is entirely optional, but we will reward 3 points of extra credit if you can complete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs2312n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the flag `use_batchnorm` is `True` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.26119551013\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 2.85e-06\n",
      "W3 relative error: 3.92e-10\n",
      "b1 relative error: 2.22e-08\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 1.01e-10\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.17e-09\n",
      "gamma1 relative error: 6.96e-09\n",
      "gamma2 relative error: 1.96e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.99653322011\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.28e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 5.55e-09\n",
      "b2 relative error: 1.39e-09\n",
      "b3 relative error: 2.23e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 5.69e-09\n",
      "gamma1 relative error: 5.94e-09\n",
      "gamma2 relative error: 4.14e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            use_batchnorm=True)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 2.340975\n",
      "(Epoch 0 / 10) train acc: 0.101000; val_acc: 0.112000\n",
      "(Epoch 1 / 10) train acc: 0.246000; val_acc: 0.220000\n",
      "(Epoch 2 / 10) train acc: 0.338000; val_acc: 0.288000\n",
      "(Epoch 3 / 10) train acc: 0.411000; val_acc: 0.290000\n",
      "(Epoch 4 / 10) train acc: 0.446000; val_acc: 0.311000\n",
      "(Epoch 5 / 10) train acc: 0.478000; val_acc: 0.297000\n",
      "(Epoch 6 / 10) train acc: 0.550000; val_acc: 0.309000\n",
      "(Epoch 7 / 10) train acc: 0.594000; val_acc: 0.309000\n",
      "(Epoch 8 / 10) train acc: 0.637000; val_acc: 0.336000\n",
      "(Epoch 9 / 10) train acc: 0.711000; val_acc: 0.343000\n",
      "(Epoch 10 / 10) train acc: 0.724000; val_acc: 0.320000\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.155000; val_acc: 0.140000\n",
      "(Epoch 1 / 10) train acc: 0.182000; val_acc: 0.160000\n",
      "(Epoch 2 / 10) train acc: 0.213000; val_acc: 0.178000\n",
      "(Epoch 3 / 10) train acc: 0.237000; val_acc: 0.229000\n",
      "(Epoch 4 / 10) train acc: 0.263000; val_acc: 0.238000\n",
      "(Epoch 5 / 10) train acc: 0.312000; val_acc: 0.258000\n",
      "(Epoch 6 / 10) train acc: 0.336000; val_acc: 0.302000\n",
      "(Epoch 7 / 10) train acc: 0.337000; val_acc: 0.264000\n",
      "(Epoch 8 / 10) train acc: 0.367000; val_acc: 0.262000\n",
      "(Epoch 9 / 10) train acc: 0.399000; val_acc: 0.277000\n",
      "(Epoch 10 / 10) train acc: 0.444000; val_acc: 0.301000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zxm/tools/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  \n",
      "/home/zxm/tools/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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T8JLf76ehYQkNDZHGAqp5JiIiIiIiIiIimeq3GWjdKXgmIiIiIiIiIiLZGDAB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\nEVEUkZ6bDoXlYwHSc9OJbJmRdshxTUmSJEkqXQZoSsypJ5wK8wp+LG1+Gpk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BWf/+cVh2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\nkiSpgv8HSajmE3fkPikAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd95f24f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o', label='baseline')\n",
    "plt.plot(bn_solver.loss_history, 'o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.train_acc_history, '-o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(solver.val_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.val_acc_history, '-o', label='batchnorm')\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers = {}\n",
    "solvers = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VSk3GjEpXDNP0rr/VzGoqZljsz6yLqI2YWsZmwFitdbjW+rJSai4Qpsz0Tgcw/fNKpCHW\nBPus7f6nlLoXk3B0IOkL1DArjkil1HhMX6cg4AmtdS2Hdadi+vppTFO09EjpffMHjiml5mGS9SuY\nvll1MEOsp2YjpsYskDuTrQ2Y4dMPaa1PJCzUWtuUUi9ZxxOplJqNqTUoh+ln8wvmvNxFax2plJoP\nDLQuYrdgRrRMqAnLaCIWATRXSg3CJJ+HrJpkV4jAfBfet85FHLBEa30tiXWXYmq2JwGbMLWuPUj/\nkPpgBjMpjhk4opu6czqz3dZ3ZinQXim1CJMcVcS8p39yu4Y+Xb8nWuvdSqkpmBqpAMwIofUw380F\nVp9WZ/jOSsTWcnt6iwHADn17aPu0/G6k6RwkYwjm5sRWpdQEzG9YUaA28Bjm/AshspK7h2eUhzzk\n4ZkPTH+neIfHVczFwPPJbPMcppnhFUxH9p3A+0Ap6/WamH4IhzB3qE9iRj6r5VBOPaucazgM751C\nvJWsdW8CDZJ4vRDmIv80Jun7EXOBfBAzCmDCemkdoj5N5VnrFsH0jTliHdNhzNw/AYnWyYNJRqIw\nCclxTNO8ConWKYapdbRhkr2xmPmF4rl7WPJLycR9P6bW5pIV+zeYZk93lGGtG4xJrs5Z7/0eYEQS\nZfpZ65wHcqfzc5bs+2aV+yEQaX2eLlv/fyGNZRfEJA4XsIY2t5Z3t/Y5OZntHsbcFDhvHfdf1vtV\nK9E6IzCDnyTeLi9mzqUzVqwLrc+EHfg/h23jgaLJfOfKJVpWBTNQyBXrtWSHq8cMHJGmz0Iy8cfj\nMLUApn/REes83orN8XOOGZXvY0xScQWTwNTFJBo/pRLjHbFYx+v425PwSDzM+htWHDGYPoCPW8d7\nIC2/J8mcAx/MjYSE72E08B4OQ/Bb+12cxHn9OfHxJvM+tcPUHp+0YjqE+S6XzMDvRlrPQVLvbXHM\n5zWa2785q4Bn0/Mdloc85JGxR8J8G0IIIUSGKKV8MbU0i7VDn6CczqoZjsQMLjHL3fEIIYTwDB7T\nJ0wp9YpS6pBS6ppSaotS6qEU1m2klPpFKXVWKRWjlNqrlBrosE4fpZRdKRVv/WtXSmV05DUhhBDJ\na4e5qz7V3YG4k1IqbxKLB2JqITYk8ZoQQogcyiP6hCmlumAmQ3wB02RgELBSKVVFm07Yjq5i2ozv\ntv7fGBivlLqitU7cp+ASpilHQoNyqfYTQggnsaYaqIFpvhWptf4llU2yu9eVUrUxTdJuYib/bQWM\n0ylPei2EECKH8YjmiEqpLcBWrfWr1nOFmdflC631x2ksYz5wRWvdx3reBzO0bVKjEAkhhMgka8CA\nHpgh45/RWu9xc0huZQ31PxyoiumPdgRTO/i+du5E3EIIIbyc22vClFJ+mNF43k9YprXWSqk1pDLM\naqIyalnrDnV4qaBSKhrT7DISeCunXyQIIYSzaK2fAZ5xdxyeQmu9BjMZthBCCJEitydhmH4EvphR\nuhI7jRnFK1lKqaOY4Zl9gXe01pMTvbwfeBbTZLEw8H/AJqVUVZ1oSGKH8ophmo5EY0YKEkIIIYQQ\nQuRMeTHTSKzUZp5Jp/GEJCwzGmOafNQHPlJKRWmtfwDQWm/BzNMCgFJqM2Zi2f6YYWmT0gqYkaUR\nCyGEEEIIIbxJD2CmMwv0hCTsLGbkqFIOy0sBp1LaUGt92Prvn0qp0sA7mIk/k1r3plJqB1A5hSKj\nAaZPn05wcHAKq3m/QYMGMXr0aHeHkeVxOLP8zJSVkW3Ts01a103Lep7y2XAFTzlW+R44Zxv5HqSf\npxynK+Jw1j4yW458DzyPpxxnTvlbkJHts2r91Nbbu3cvPXv2BCtHcCa3J2Fa6zilVATQDFgCtwbm\naIaZRDCtfDGTnSZJKeUDVMdMqJqcWIDg4GBCQkLSsWvvU7hwYY84xqyOw5nlZ6asjGybnm3Sum5a\n1vOUz4YreMqxyvfAOdvI9yD9POU4XRGHs/aR2XLke+B5POU4c8rfgoxsn1Xrp6Ncp3dTcnsSZvkM\n+N5KxhKGqM8PfA+glPoAKJNo5MOXMaNO7bO2bwoMBj5PKFAp9TamOWIUZtb514FyQOIh7HOsbt26\nuTsEIOvjcGb5mSkrI9umZ5u0rusp77un8JTzId8D52wj34P085Rz4Yo4nLWPzJYj3wPP4ynnIqf8\nLcjI9lm1vjvfe48Yoh5uJVavY5oh7gT+rbXebr02GSivtX7Mej4A07erAmYulgPAeK31+ETlfYaZ\nQLQ0cAGIAIZqrXenEEMIEBEREeERd0SEcIfQ0FCWLFni7jCEcCv5Hggh3wMhIiMjqV27NkBtrXWk\nM8v2lJowtNZfA18n89ozDs+/Ar5KpbzXgNecFqAQQgghhBBCOIGPuwMQQngWT2mWIYQ7yfdACPke\nCJGVJAkTQtxB/ugKId8DIUC+B0JkJUnChBBCCCGEEMKFJAkTQgghhBBCCBeSJEwIIYQQQgghXEiS\nMCGEEEIIIYRwIUnChBBCCCGEEMKFJAkTQgghhBBCCBeSJEwIIYQQQgghXEiSMCGEEEIIIYRwIUnC\nhBBCCCGEEMKFJAkTQgghhBBCCBeSJEwIIYQQQgghXEiSMCGEEEIIIYRwIUnChBBCCCGEEMKFJAkT\nQgghhBBCCBeSJEwIIYQQQgghXEiSMCGEEEIIIYRwIUnChBBCCCGEEMKFJAkTQgghhBBCCBeSJEwI\nIYQQQgghXEiSMCGEEEIIIYRwIUnChBBCCCGEEMKFJAkTQgghhBBCCBeSJEwIIYQQQgghXEiSMCGE\nEEIIIYRwIUnChBBCCCGEEMKFJAkTQgghhBBCCBeSJEwIIYTIYlprd4cghBDCg0gSJoQQQmQBm81G\n2OthBIUEEVg3kKCQIMJeD8Nms7k7NCGEEG6Wy90BCCGEENmNzWajQcsG7K28F3uoHRSgYezBsaxt\nuZbNqzbj7+/v7jC9jtYapZS7w0iRN8QohHA/qQkTQgghnGzoe0NNAlbZSsAAFNgr2dlbeS/DRg5z\na3zexBtqFL0hRiGEZ1HSTv02pVQIEBEREUFISIi7wxFCCOGlgkKCiA6Nvp2AJaahzKIyHIo4RG7f\n3K4OzavcUaNY6XaNos9BH4L/DvaIGkVviFEIkTGRkZHUrl0boLbWOtKZZUtNmBBCCOFEWmvifOOS\nTsAAFJyIPUHB9wtSd0JdXlr6EhMjJ7Lz1E7i4uNcGqun84YaRW+IUQjheaRPmBBCCOFESin84v1A\nk2xNWOk8pXmz5ZtEnIxgw5ENjI8cj13byeObhxqla1DnnjrULlObOmXqULVEVXL55Mw/1+Frwk2f\nuiTYK9n5YeEPtO3f1sVR3WnOijnY2ycf45LwJYxhjIujEkJ4upz5qy6EEEJkobbN2/LVga/Qle9u\n8u9zwIfOrTsTVi/s1rIrN66w89ROIk5EsP3kdn6O/plvtn+DRpM3V15qlq5J7XtMUlb7ntoElwjO\nVolZXHwcRy4d4eCFgxy4cMD8e/4Ax2KPpVijePr6aVpMbZH8OllNAzdIMcY4nzgZrEMIcZfs8wsu\nhBBCeIj6nevzZZcvUSh0JX27n9ABH4Kjghn59cg71i+YuyCNyzWmcbnGt5bZrtvYeWon209sJ+Jk\nBGsOruHr375Go8mXKx81S9e8lZTVKVOHB4o/gK+Pb4ZjzupE4WLsRQ6cNwlW4mTr4IWDHLl0hHgd\nD4Cv8qV8kfJUDKhIXnterugrydYo3pv3XjYO3JhlMadFk0VNOKaTSRY1XLt2jcvXL1M4b2GXxyaE\n8FyShAkhhBBOFH0xmld+eoW2w9sStCeIJeFLiPOJw8/uR2jzUEZ+PTJNAzX45/GnSfkmNCnf5Nay\ny9cvs+PkDiJORrD9xHZWRK3gy21fApDfLz+1Ste6XWNWpjb3F7s/xcTMZrMx9L2hhK8JJ843Dr94\nP9o2b8uot0elezCJm/abHLt87I5E6+DFg7eeX4i9cGvdQnkKUSmgEpWKVqLTPZ2oGFCRSkUrUTGg\nIoGFAvHz9QMg7Pcwxh4cawa8cOBzwIf2rdpToUiFdMXpbO1atks2RqLgfPHzlPmsDF2rdaV/nf48\nVOYht9eKSc2cEO4noyMmIqMjCiGEyIwb8TdoMrkJZ66eIbJ/JEXyFgGy9qL3UuwlIk9GEnEy4lZy\nFnU+CoACfgWodU+tO/qYVSlWBR/lk6FR/S5fv3y7Jish2bISrcOXDnPTfhMAH+VDYKFAk1wFmOQq\n4VGpaCUC8gak6XwkG6NVo+gJIw+mFuP8efOZEzWHCZETOHr5KDVL16R/7f70qN4D/zyui91mszF0\n6KeEh/9KXFwB/Pyu0rZtI0aN+o/bz6EQniorR0eUJCwRScKEEEJkxuCVg/ly25f8+uyvPFT2IbfF\ncTH2oknMrD5m209s5+CFg4Bp+hhyTwiXV1xml9+upPutRfnwqN+j1O9Z/45mg2djzt5ap2Dugncl\nWQn/L1+kvNOG37fZbAwbOYwlaxxqFIelrUbRFdISY7w9nhVRKxgXMY4f//6RfLny0b16d/rX7k/t\nMrWzPL4GDTqwd+9r2O2tSMgUfXxWEhz8GZs3z/eYcymEJ5EkzEUkCRNCCJFR4fvDCZ0dyuhWoxlY\nf6C7w7nL+Wvn70jMFr65kPie8cn2ZWIa3Dvg3rsSrIT/F89f3OVN2ryhGV1aYjx66SgTd0zku8jv\nOG47Tu17atO/dn+6Ve9GwdwFnR5TWNgIxo5tgN3e+q7XfHyWM2DAVsaMecfp+xXC20kS5iKShAkh\nhMiIo5eOUnNcTRqXa8yiLou8IlEIrBvI8TbHk12nzNIyHNt2zOOPxZvdtN9k2d/LGBcxjuV/L6dg\n7oL0qN6D/nX6U7N0TaftJyioOdHRq0ku465QoSWHDq122v6EyC5ksmYhhBDCQ8XFx9F1flcK5i7I\n5Kcme0XScsdcZknRkDs+t1ccizfL5ZOL0PtD+bH7jxx69RCv1nuVxfsXU2tcLep9V49JOyZx9cbV\nTO1Da8316wVIaRz9uLj8yE15IVxLkjAhhBAiE97++W22Hd/G7A6zKZqvqLvDSbO2zdviczDpywCf\nAz6Etgh1cUQ5W/ki5Xnvsfc4PPAwCzovICBvAP2W9KPMZ2UYsGwAv5/+Pd1l2mzwwQeK06evklLG\n7ed3VRJuIVxMkjAhhBAig5b/vZyPfv2I9x97nwaBDdwdTrqMensUwX8H4xPlc/v6XJtBOYKjghk5\nbGSK24us4efrR7vgdqzouYIDYQd45aFXmLdnHg9++yANJzZkys4pXIu7lmIZV67Ahx9CUBD8979Q\nrVojfHxWJrmuj88KQkMbJ/maECLrSBImhBBCZMDxy8fpvag3T9z3BIMbDnZ3OOnm7+/P5lWbGVBm\nABXCK1B2aVkqhFdgQJkBHjH0u4CggCDeb/Y+RwYdYW6nuRTIXYC+i/tS5rMyvLr8Vfac2XPH+leu\nwEcfQYUKMHw4dO4MUVHw66//ITj4M3x8lpM44/bxWU5w8GhGjvS+z68Q3k4G5khEBuYQQgiRFjft\nN3lsymMcvHCQnS/upHj+4u4OKdO8YeRBAVHno5gQMYHJOydzJuYMjcs1pu+/+nP6546M/iQvly7B\ns8/CW29BuXK3t7PZbAwb9j+WLPmVY8fykydPDM8914iRIwdLwi1EMmR0RBeRJEwIIURaDFs7jA9/\n+ZB1fdfRuJw05RKud/3mdX7YvYh3fxzHAfvPEFOU6vY+jO75As1qPJDitsOH2xkzxoczZyC3c6Zz\nEyJbktERhRBCCA+x+sBq3t/4Pu89+p4kYMItYmJg7Bd5+L/Hu3D43bV0OrWffnWe4USJqTRfFMwj\n3z/CrN9ncf3m9Vvb2Gw2wl4PIygkiHHh5bjsG0SnXmHYbDY3HokQOZfUhCUiNWFCCCFSctJ2kprj\nalKzdE2W91iOj5J7mcJ1rl2Db781/b7OnoW+fWHoUDMAB0DszVgW7F3AuIhxbDi8geL5i9O3Rl+6\nV+lOr2692Ft5L/ZKdjNavQaifKh2IFj6AAqRDKkJE0IIIdws3h5PjwU98FW+TGs3TRIw4TLXrsGY\nMVCxIvzf/8ETT8Bff8F3391OwADy5spL9+rdWd93PXte3kPP6j2ZuGMiIX1D+LPSn9gr229PF6aA\n++zsrbxu1KjOAAAgAElEQVSXYSOHueOwhMjR5C+IEEIIkQYjN4xk/eH1zOwwk5IFSro7HJEDxMbC\nF19ApUoweDC0bg379sGkSSYhS0lwiWBGtx7N8deOU/xscaic9Hr2SnaWrFni/OCFECmSJEwIIYRI\nxc+Hfua/6//LiKYjeKTCI+4OR2RzsbHw5Zcm+Ro0CFq0MMnX5MlQOZlkKjl5c+UlT748t2vAHCmI\n84lDuqcI4VqShAkhhBApOH3lNN0XdOfRoEcZ2mSou8MR2dj16zB2rEm0Bg6EZs1g716YMiX9yVcC\npRR+8X63pwdzpMEv3k+mJxDCxSQJ8zJyp0oIIVzHru30WtgLrTUz2s/A18fX3SGJbOj6dfjmG5No\nhYXBI4/Anj0wdSpUqZL58ts2b4vPwWQu+f6GkhUry/WFEC4mSZgXSDysbGDdQIJCggh7XYaVFUKI\nrPbhLx+y5uAaZrSfQemCpd0djshmbtwwox3edx+88go8/DD8+SdMnw733++8/Yx6exTBfwfjE+Vz\nu0ZMg0+UD2p9EbZVWUPvRb2xXZfrirSSpFVkliRhHs5ms9GgZQPGnhxLdGg0x9scJzo0mrGnxtKg\nZQNJxIQQIotsPLyRt39+m2EPD6NZxWbuDkdkIzduwPjxJvl6+WVo1MgkXzNmwAMpz7OcIf7+/mxe\ntZkBZQZQIbwCZZeWpUJ4BQaUGUDPx45QOmIai/Ytovb42uw4ucP5AWQTclNcOJPHJGFKqVeUUoeU\nUteUUluUUg+lsG4jpdQvSqmzSqkYpdRepdTAJNbrZL12TSm1Syn1eNYehfMNfW+omdfDYVhZeyUZ\nVlYIIbLK2ZizdJvfjcblGjO86XB3hyOyibg4mDDBNDF88UVo0AB+/x1mzYLg4Kzdt7+/P2M+GsOh\niEMc3XaUQxGHGPPRGLp08efUqp788GgkBXMXpP7E+ny59Uup6XEgN8WFs3lEEqaU6gL8DxgB1AJ2\nASuVUsWT2eQq8CXQBHgAeA8YqZTql6jMhsBMYAJQE1gMLFJKVc2q48gK4WvCzcSKSZBhZYUQwvns\n2k7vhb25Hn+dme1nkssnl7tDEl4kqeQlLs7M6VWlCrzwAtStC7t3w+zZUK2a62NMPAhHs2bg7w8R\nq+9j83ObebH2i4StCKP9nPacv3be9cF5KLkpLpzNI5IwYBAwTms9VWu9D3gRiAGeTWplrfVOrfUP\nWuu9WusjWuuZwEpMUpYgDFiutf5Ma71faz0ciAQGZO2hOI/WmjjfuBSHlb2ir2C3J52kCSGESL9P\nN33K8qjlTGs3jbKFyro7HOEFbDYbYWEjCApqTmDg0wQFNScsbATnz9uYNMn073r+eahTxyRfc+bA\nv/7l7qiNvHnhySdhwQLIkysPYx4fw6Iui1gfvZ5a42qx6egmd4foEeSmuHA2tydhSik/oDbwU8Iy\nbW4jrQEapLGMWta66xItbmCVkdjKtJbpCZRS+Mb7pjis7NmLZ6k9oTZTdk7h+s3rLo1PCCGym01H\nN/HWT28xpNEQWldu7e5whBew2Ww0aNCBsWMbEB29muPHFxMdvZqxYxtQunQHnnvORq1asGsXzJ0L\n1au7O+K7tW8PO3fCwYPm+VMPPMXOF3cSWCiQhyc/zAcbP8Cuc+4N37TcFJe51kR6uT0JA4oDvsBp\nh+WngRSHolJKHVVKxQLbgLFa68mJXi6dkTI9ydmYs1y/5zpEJf26zwEfnm75NPcUvIe+i/tS7vNy\nvLPuHU5dOeXaQIUQIhs4F3OOrvO6Uv/e+rz32HvuDkd4iaFDP2Xv3tew21uTuJ2a3d6auLhBdO/+\nP+bPhwcfdGeUKXv8cVMjtnDh7WXlCpdjXd91vNHoDYauHUrr6a05fcXxsipnWHtoLWcunZG51oRT\neUISlhmNMbVoLwKDrL5l2UL0xWgaTWpEfIN4Ku6vmOSwssFRwUz9ZCrLeixj3yv76BjckU82fUK5\n0eXos6gPkScj3XoMQgjhLbTWPLP4Ga7GXWVWh1nSD0ykWXj4r9jtrZJ5tTWbNv3q0ngyomBBaNXK\nNElMLJdPLkY1G8WqXqvYfXo3Nb6tweoDq90TpBvsOLmDVtNb0XxacwLuC8DnQDKXzVFQploZqQkT\n6eIJSdhZIB4o5bC8FJBilY7W+rDW+k+t9URgNPBOopdPZaRMgEGDBhEaGnrHY9asWalt5jS7T++m\n4cSG3LTfZMvLW9i5dmeSw8puXrUZf39/AO4vfj9jnxzLsUHHeL/Z+6yPXk/t8bV5ePLDLNi7gHh7\nvMviF0IIb/P5ls8J/yucqU9PJbBwoLvDEV5Ca01cXAFSaqcWF5ffKy7O27eHTZvg5Mm7X2tesTm7\nXtzFg6UepNX0Vrz101vExce5PkgXOXD+AN3mdyNkfAiHLx5mQecF/DXrL4Kjkp5rrfiu4mwK3ES7\nH9pxMfaiW2MXGTdr1qy7rv8HDRqUZftTnvDDoJTaAmzVWr9qPVfAEeALrfUnaSxjONBXa13Rej4b\nyKe1firROr8Cu7TWLydTRggQERERQUhISKaOKaPWR6/nqdlPUTGgIst7LKdUwTvzSK11mqq7b9pv\nsnjfYsZsHcPGIxspX7g8/677b54LeY4ieYtkVfhCCOF1th3fRuNJjXm13qt80jJNf3KEuCUoqDnR\n0atJOhHTVKjQgkOHHLuoe57z56FUKfjiC3jppaTXsWs7H//6McPWDqPevfWY2X4m5YuUd22gWej0\nldO8t+E9xkWMo2SBkvz3kf/St2bfWzXjNpuNYSOHsWTNEuJ84vCz+xHaPJSRw0ay7sQ6ei3sRfH8\nxZnXeR41S9d078EIp4iMjKR27doAtbXWTm1i5ilJWGfge0yzwm2Y0RI7Ag9orc8opT4Aymit+1jr\nv4xJ0vZZRTQFPgM+11qPsNZJGKjjTeBHoBswBAjRWu9JJg63JmEL9y68NS/Ngi4LKJSnkFPKjTwZ\nyZitY5j1+yxy++amb82+hNULo0qxKk4pXwghvNXF2IvUGleLUgVKsfGZjfj5+rk7JOFlQkJGsGNH\nA+DugVx8fJYzYMBWxox5x+VxZUTLlqA1rE6lxeGmo5voNr8bl69fZvJTk3n6gaddE2AWuXz9Mp9u\n+pTPNn+Gn68fQxoN4d/1/k1+v/zJbpPUTfGDFw7ScU5H9p7dyzdPfkPfmn2zOHKR1bIyCUNr7REP\n4GUgGrgGbAbqJHptMrA20fMBwO+ADbgAbAdeSKLMDphE7RqwG2iVSgwhgI6IiNCu9u1v32qf//ro\nznM769i42CzZx0nbST187XBd4uMSmnfQT8x4Qq+KWqXtdnuW7E8IITyZ3W7X7Wa300U+LKIPXTjk\n7nCEF/riC63hsi5duoX28Vmmwa5NGmPXPj7LdLVqLfTly5fdHWaaffON1r6+Wp87l/q652LO6adn\nP615Bz3gxwH6Wty1rA/QyWLjYvXnmz/XxT8urvOOzKtfX/W6Ph9zPlNlxtyI0c8tfk7zDrrf4n5e\neV7EbRERERrTADVEOzn38YiaME/hjpowrTXvbXiPEetGMOChAYx5fAw+Kmu76sXejGX2H7MZs3UM\nO0/tpGqJqrxa71V6Ptgzxbs+QoicSaexGbS3+XLrl4StCGNhl4VefydfuN6iRaYf1WuvwYgRNoYN\n+x9LlvxKXFx+/PxiCA1txMiRg2/13fYGJ09C2bIwaRL07Zv6+lprxv42lsGrBlO1RFV+6PiDV7Sy\nsWs7M3+fyds/v82RS0d4puYzvPPIO9xb6F6n7WPyjsm8vOxlgosHM6/zPCoGVHRa2cJ1sn1zRE/h\n6iQs3h7PgGUD+DbiW0Y9Noo3G7/p0gsdrTUbDm/g862fs3jfYgLyBfBCyAu8UvcVp/4QCSG8j81m\nY+h7QwlfE06cbxx+8X60bd6WUW+P8qqLyuREnIig4aSGvFTnJT5v/bm7wxFeZssWePRRaNsWZs8G\nn0T3Tr39pkXjxlC0KCxJx9zDO07uoMu8LpywneDbNt/S88GeWRdgJmitWRG1giE/DWH36d08/cDT\nvP/Y+wSXCM6S/e08tZMOczpw/tp5prWbRpsqbbJkPyLrSBLmIq5MwmJvxtJjQQ8W7VvE+DbjeS7k\nuSzdX2oOXjjIV9u+YuKOiVy9cZWOVTsysP5A6t9b361xCSFcz2az0aBlA/ZW3ou9kt2MN6DB56AP\nwX8H3zEyqze6FHuJkPEhFM1XlF+f/ZXcvrndHZLwIlFR0KAB3H8/rFlj5tfKTj77DN56C86cgfR8\nzW3Xbbyy7BWm7Z5Gnxp9+OqJryiYu2DWBZpOW49t5Y01b7D+8HqalGvCR80/okFggyzf78XYi/RZ\n1Icl+5fwVuO3ePfRd/H18c3y/QrnyMokzBOGqM9xLsVeovX01iz7exkLuyx0ewIGUDGgIp+1+oxj\ng44xutVoIk5G0GBiA+p9V49Zv8/K1kPRCiHuNPS9oSYBq2xPPPcs9kp29lbey7CRw9waX2ZorXk+\n/HnOxpzlh44/SAIm0uXMGTOxcbFisHhx9kvAANq1g+vXYfny9G3nn8efqe2m8v1T3zN3z1zqjK/D\n7tO7sybIdNh3dh8d5nSg/sT6nL92nqXdlrK+73qXJGAARfIWYWGXhXzY7EM+/PVDWk5vyT9X/3HJ\nvoVnkyTMxU7aTtL0+6bsOr2LNb3WEHp/qLtDuoN/Hn/+Xe/f7B+wn/Bu4fjn9qf7gu5UGFOB9ze+\nz9mYs6mWIbWrQni38DXhpgYsCfZKdpasSUc7JQ8zLmIcc/fMZVLoJOmjIdLl2jUIDYXLl2HZMpOI\nZUdBQVCr1t0TN6dVn5p9iHghgjy58lB3Ql2++e0bt1wXHL98nOeXPE+1r6ux/cR2pjw9hR39d/Bk\nlSdd3lzUR/nwRuM3+Kn3T/z5z5/UGleLX494/iTeImtJEuZCf537i4aTGnI25iy/PPMLjco1cndI\nyfJRPrSp0oY1vdew+8XdPFH5Cd7b8B6BowN5fsnz/PHPH3esb7PZCHs9jKCQIALrBhIUEkTY62HY\nbDY3HYEQIr3i7fFsPbaVc/HnUpp7ljifOK+82bLz1E4GrhjIKw+9QoeqHdwdjvAi8fHQowfs3g1L\nl0LFbJ6/d+gAP/4IsbEZ2/6B4g+w5bktPFvrWV5e9jKd5nZy2STGF65dYMiaIVT+sjIL9y3k0xaf\nsn/AfnrX6O32ZoCPVHiEyP6RVAyoyCNTHuHzLZ975W+pcA7pE5ZIVvYJ++34bzwx8wlK5C/Bip4r\nKFe4nFPLd4WzMWcZHzGesb+N5YTtBM2CmjGw/kCalG5Co1aNsm3/ESGyswPnD7D64GpWH1zN2kNr\nuRh7ETVVoXvp5OaeJf/s/MwNn0vLSi1vTWLq6WzXbdQeX5sCuQuw+bnN5M2VDduRiSwzcCB8+aVp\ngtgmB4ytsHcvVK0K4eGZP975e+bz3JLnCMgXwOwOs6l3bz3nBOngWtw1vtr2FR/88gHX46/zWv3X\n+E/D/1A4b+Es2V9mxMXH8eZPb/K/zf+jU9VOTAydiH8euU7yRDIwh4tkVRK2MmolHeZ0oHqp6izt\ntpRi+b27DUNcfBzz9szj862fs+34NgpvKszlkpfRle/+LPlE+TCgzADGfDTGDZEmzxtGr/KGGIX3\nORdzjrWH1rL64GrWHFzDoYuH8FW+1L+3Pi0qtqB5xebM+nIW35z+JskmiSpKEXAugPP1zlOqQCm6\nV+9Onxp9qFG6hhuOJm201vRc2JMl+5cQ+UIk9xW7z90hCS8yerQZhv6bb+DFF90djesEB5sBSCZN\nynxZ0Rej6Ta/G9tPbOf9x95ncMPBTpuO56b9JlN3TWXEuhGcunKK50OeZ3jT4ZQuWNop5Wel+Xvm\n88ziZyjjX4b5nedTrWQ1d4ckHEgS5iJZkYTN2D2Dvov70qpSK+Z0mpPt5uHacmwLzZo3I6ZrTLJ3\nzSuEV+BQxCGXx+bIG4bc9oYYhXeJvRnLpqObWH3A1HZFnoxEo3mg+AO0qNiCFhVb0LRCUwrlKXRr\nm2RHRzzgQ3BUMJtWbuLA1QNM3TWVGb/P4EzMGR4s9SB9avShe/XuHnfx813kdzwf/jyzOsyi67+6\nujsc4UXmzYPOneH11+HDD90djWsNHQrffgunT0MuJ1R4x8XH8fbPb/PRrx/RunJrpjw9hZIFSma4\nPK01i/cv5q2f3mLv2b10qdaFkY+NpHLRypkP1oX+OvcXHeZ04OCFg4xvM54eD/Zwd0giEUnCXMTZ\nSdjozaN5bdVr9KnRhwltJ+Dn65f5ID2M1prAuoEcb3M82XXUbMW9L9xLsfzFCMgbQEC+APOv9f+i\n+Yreudz6t0jeIk5rv+0NQ257Q4zC89m1nd2nd7Pm4BpWH1zNxsMbuXbzGiULlKR5xea3artSmwvQ\nZrMxbOQwlqxZQpxPHH52P0KbhzJy2Mg7Podx8XGsPLCSKbumsGT/EuLt8bSq3IreD/Ym9P5Q8vnl\ny+pDTtHvp3+n7nd16f1gb8a1HefWWIR3+fVXaNbMTMg8ffqdc4HlBBERUKcO/PQTPPaY88pdGbWS\nXgt7kcsnF9PbT+exoPQXvuHwBoasGcLmY5tpXrE5Hzb7kNplajsvSBe7euMqL/34EtN2T+PlOi/z\nWavPyJMrj7vDEkgS5jLOSsK01gxZM4SPN33MkEZDeL/Z+9m6WVlQSBDRodHJ1oQFzA3gpS9e4kLs\nBfO4Zv49f+08F65d4GLsReJ1fJJlF85T+K7kLKmkzXFZ4byF72jqEPZ6GGNPjjVDbjvwlCaT3hCj\n8ExHLx291bxwzcE1nIk5Q75c+WhaoSnNg5rTolILqpesnuHfobQ2jb1w7QJz/pzDlF1T2HxsM4Xz\nFKZztc70rtGbRoGNXP47eOXGFR6a8BC5fXOz5bktbk8IhffYvx8aNoTq1WHlSsiTA6+HtYYKFUyf\nsLFjnVv2SdtJei7syc+HfmbYw8MY3nT4Hf1Lk/vN+f3077z505v8+PePhNwTwofNPqRFpRbODc5N\ntNaMjxhP2IowapauydxOc71y/IDsRpIwF3FGEhYXH0e/8H5M3TWV0a1GM7D+QOcG6YHCXg9j7Kmx\nSfYfSUvyoLXGdsN2Kzm7cM1K0BIlbHckbomeX4y9iF0n0W8FReG8hW8lZ3/+709iu8UmmygWmVuE\nV8e+mpnTkGmfv/w5lzpf8vhmncK5MtL371LsJdZFr7uVeO0/tx+Fok6ZOrdquhoGNnTrndS/z/3N\n1F1TmbZ7GocvHaZiQEV6P9ibXjV6uWxo+D6L+jB/z3wiXojg/uL3u2SfwvudPm36QuXNa2rDAgLc\nHZH7DBoEP/wAx445vyYw3h7PB798wIh1I2gU2IjxLcfz9Zivk2yOfz7+PMPXDWfarmlUDKjIqMdG\n0alaJ6f1K/Mk209sp+Ocjly5cYUZ7WfQqnIrd4eUo0kS5iKZTcKu3rhKp7mdWHNwDVOenkK36t2c\nH6QHSq3/SFY2o7NrO7brtjtq1hyTt/PXzjP1zanEdkp+rF2f2T7c8/w9bqux1FpzcsJJ7F2TnpsJ\noOzSshzddjRb16rmFOnt+xcXH8fW41tv9evadnwb8TqeigEVbyVdjwU9RtF8Rd1wNCmzazsbDm9g\n6q6pzN0zlys3rtCkXBN61+hNp6qdsmzksik7p9B3cV+mtZtGzwd7Zsk+RPZz9So8+igcPQpbtkD5\n8u6OyL02boSHH4ZNm0ximiX7OLyRrjO7cmrSKXR9ja6k72iOH7AjgMtPX6Zo4aIMbzqcfiH9sv0k\n6+diztFrYS9WRK1gRNMRvN307WyZcHoDScJcJDNJ2LmYczw580n++OcPFnZZmG2qx9Mqrf1H3CW1\nJpMVllTgUKR7a5m8IUaReWnp+1ewYEH2nd13a+j4ddHruHLjCgF5A2hWsdmtxMvbJhuOiYth4d6F\nTN09ldUHVpMnVx6efuBp+tToQ/OKzZ023P2eM3t4aMJDdK3WlYlPTXRKmSL7i4+Hdu1g7VrYsAGc\nPFONV4qPhzJloHdv+OSTrNvPC6+9wIR/JkBSA5f+DfWpz+pJqymYu2DWBeFh7NrOqA2jGLFuBC0r\ntWR6++kUz1/c3WHlOJKEuUhGk7Ajl47QanorzsWcY1mPZdQpUyfrgvQCnji0emabTLqCN8QoMi+1\nvn9VrlXB1tDGcdtxcvvmplFgIzOKYaUW1Cpdy+2TjTrL8cvHmfH7DKbsmsKeM3soXbA0Pav3pHeN\n3lQvVT3D5cbExVB3Ql00mm39tlEgdwEnRi2yK61hwAAYN87MjfX44+6OyHP07w9r1kBUFGTVn/ZU\nb0Lm4Ob4qw+sptv8bhTIXYC5neZSt2xdd4eUo0gS5iIZScL++OcPWk1vRR7fPKzsuVLmn/FQ7mwy\nmdkYiYIyf5Rh38Z9bo9RZF5qFxt+M/0I+yqMFhVb0KR8k2w3rYUjrTWRJyOZumsqM/+YydmYs9Qs\nXZM+NfrQ7V/dKFWwVJrLUUrRb0k/Zv4+k9+e/03m3BFp9sknZhj6CROgXz93R+NZVq2CVq1g506o\nkQXTAaZllOWc3hz/yKUjdJ7bmciTkXze+nNeqvNSjj0XrpaVSZg0MM2EjYc30mRyE0rkL8Gm5zZJ\nAubB/P392bxqMwPKDKBCeAXKLi1LhfAKDCgzwCMSsJRirB5XndNtTrPtzDZ3hygy6Wb8Ta5wJekE\nDEBBycIl+aTFJ7Sq3CrbJ2AASilql6nNmMfHcPy14yzuupiKARV5ffXrlP2sLG1ntWXun3OJvXl3\nn06bzUbY62EEhQQRWDeQEv8qwcTPJvJp008lARNpNnu2ScCGDZMELCmPPAJFisCCBVlTvlIKv3g/\nc9MxKRr84v1ydNJRrnA5Njyzgf61+/PKslfotbAXV29cdXdYIpOkJiyR9NSELd63mK7zu1L/3vos\n6rIoyzqXi6zhiU0mHSXEeNN+kydnPsnWY1vZ/NxmgksEuzs0kQ7x9ng2HtnInD/nMH/vfP75+h/o\njfT9S8W5mHP88OcPTN01la3Ht1IkbxG6VOtC7xq9aXBvA65cuZJszXG1A9U85uaK8Gzr10PLltCl\nC0yZknXN7bxd796wYwf8/nvWlC/N8dNu1u+z6Bfej6AiQczvPF9Gfs1i0hzRRdKahH0X+R39l/an\nfXB7prWbRt5ceV0XpMiRLsVeotGkRsTExbC131ZKFCjh7pBECuLt8fx69Ffm/DmHeXvmcfrqacoV\nLkfnqp05svgI82zz5GIjHfaf3X9ruPujl49SuWhlim0pxm8+v8m8eiLD9u41c4GFhMDy5ZA7ew+4\nlymLFplBS/bvhypVnF++N3QZ8CR7zuyhw5wOHL98nElPTaJj1Y7uDinbkiTMRVJLwrTWjNo4ird/\nfpuX67zMF49/kW06yQvPF30xmnrf1aNy0cr81PsnSf49jF3b+fXIr7dqvE5eOUlgoUA6Ve1E52qd\nqVu2LkopudjIBLu2sy56HVN3TWXq4KnoXlo68osMOXnSDLnu7w+//AKFpTFLimJioEQJGD4c3ngj\na/bh6aMsexrbdRv9wvsx5885DKo/iI+af4Sfr5+7w8p2JAlzkZSSsHh7PK+ueJWxv43l3UfeZdjD\nwzy+OZvIfrYc28KjUx6l3QPtmNF+hnwG3cyu7Ww+utnUeO2dxwnbCe4tdC+dqnaiU9VO1Lu3XpJz\nu8jFRuZorSlbtywn25xMdp2c3pFfJO/KFWjaFE6dMnOBBQa6OyLv0LEjHDkC21zQPdkbugx4Aq01\nX277ksGrBlOvbD1+6PgDZQuVveN1OY+Zk5VJmHMmZcnmrt+8Ts+FPVmwdwHj24zn+drPuzskkUPV\nv7c+U56eQpd5XahSrArvPPKOu0PKcezazpZjW241NTxuO04Z/zK3arzq31s/1Uk1/f39GfPRGMYw\nRv5IZoBSijzxeUwfsORGmczhHflF0m7eNP2//v7bTEQsCVjatW8PPXqYRKxcuazdl3x300YpRVi9\nMOqUqUPnuZ0JGR/CpFaTWDltJeFrwonzjcMv3o+2zdsy6u1RcpPPw0gSlopLsZdo90M7Nh3dxPzO\n83n6gafdHZLI4TpX60zU+SiGrh3KfUXvo8eDPdwdUrantWbr8a3M+XMOc/fM5djlY9xT8B5T41Wt\nEw0DG6aaeCVHLjYypm3ztow9mExH/gM+hLYIdUNUwpNpDS+/bIZcX7Ysa4Zbz86efBL8/Ez/sLAw\nd0cjEmsY2JDI/pF0nt6ZNk+3QTVU6FB9q7n72INjWdtyrTR39zCShKXg1JVTPD7jcQ5dOMSqXqt4\nuPzD7g5JCADebPwmf5//m2eXPEv5IuVpXK6xu0PKdrTWbDu+7VZTwyOXjlC6YGk6Bnekc7XONCrX\nKMOJl8i8UW+PYm3LtezVSfetG/n1SHeHKDzMBx+YecAmT4YWLdwdjfcpXBiaNzdD1UsS5nlKFihJ\n9ajqrG+wHl05UVcjBfZKdvbqvQwbOUwGLPIg0icskcR9wgpVKETLaS25Hn+dFT1WUL1UdXeHJ8Qd\nbsTfoOW0lvzxzx9s7beVSkUruTskr6e1ZvuJ7bdqvA5fOkypAqXoENyBztU607hcYxmMx4NI3zqR\nVtOnQ69e8M47MGKEu6PxXt99B/37m4FNSpZ0dzTCUVBIENGh0TJgkRPJwBwukpCEFa9cnCvlrxDY\nOpDV/VZTvkh5d4cmRJLOXztP/e9MH6TNz20mIF+Au0PyKGnpb6W1JuJkxK3EK/piNCULlLyVeDUp\n10QSLy8gfetEctauhdatoWdPmDhR5gLLjDNnoHRpGDdOJrb2NFprAusGcrzN8WTXkQGL0i8rkzBp\nS5OEs4+dJbZ0LL5zfSnqW9Td4QiRrKL5ivJj9x85E3OGDnM6cCP+hrtDcjubzUbY62EEhQQRWDeQ\noJAgwl4Pw2az3VpHa03kyUiGrBlCpS8q8dCEh/h+5/e0qtSKn3r/xPHXjvP1k1/zSIVHJAHzEnJR\nISMvqGMAACAASURBVJLyxx9mfqtHHzWJg3xMMqdECXj4YdMkUXgWpRR+8X5mwKKkyIBFHkf6hCXn\nPvhL/SXtZ4XHu6/YfSzsspDmU5vz0tKX+C70uxz7I3vHHFyh9rs6JX879VuWHVnGnD/ncODCAYrl\nK3arxqtphabk8pGfRCGyi+PH4fHHISgI5s41g0qIzGvfHgYPhkuXZH41TyMDFnkXaY6YSEJzRF4A\nyiDtZ4VXmbprKn0W9eHDZh/yRuMsmk3Tw4W9HsbYk2OxV777DxB/A8eg6BNFaf9AezpX68yjQY9K\n4iVENnT5sqmxOXfOzAVWtmzq24i0OXrUDFE/YwZ07+7uaERid9yITDRgEVHgH+nPsS3HKFSokLvD\n9CrSHNFdFMT5xCGJqvAGvWv0ZliTYQz5aQjz9sxzdzhuEb4mPMk7gABUhlIXSnFq8CkmhE6gRaUW\nkoAJkQ3FxUGnThAdDcuXSwLmbIGBULeuNEn0RP7+/mxetZkBZQZQIbwCZZeWpUJ4Bdrkb4PtaRvT\n9093d4giEbkCSYm0nxVe5t1H3yXqQhS9FvaiXOFy1C1b190huYzWmjjfuKRHhQJQkCt3Lkm8hMjG\ntDaj9/38M6xYAf/6l7sjyp7at4d334WYGMif393RiMT8/f0Z89EYxjDmjgGLBiwbwGsrX6NxucY8\nWOpBN0cpQGrCUiTtZ4W3UUox+anJ1Cpdi9BZoRy5dMTdIbmMUgp9Q0unZCFysPfeM/OATZoEjz3m\n7miyr3btTAK2cqW7IxEpSfz37pMWn1ClWBW6zutKTFyMG6MSCSQJS4ZPlDXh5zCZ8FN4l7y58rKo\n6yLy+eXjyZlPcvn6ZXeHlOW01ozbPo5TRU9BVNLryE0VIbK37783c4CNGmWGoxdZp0oVU8soTRK9\nRz6/fMzuOJvoi9EMWjHI3eEIJAlL0j0b7mFAmQFsXrVZJvwUXqlkgZL82P1Hjlw6Qpd5Xbhpv+nu\nkLLMpdhLdJnXhRd/fJE+L/UhOCoYnyif2zViWm6qCJHdrVoFzz9vHm++6e5ocob27SE8HG7IzChe\no2qJqoxpPYbxkeNzbN9xTyJJWBKWzljKmI/GSAImvFrVElWZ12keqw+s5tXlr2bLAWa2Hd9GrXG1\nWHlgJXM6zmFS50lsXb31rk7JclNFiOxr1y7o2BFatoSvv5a5wFylfXszTP3PP7s7EpEe/UL60bFq\nR54Pf57DFw+7O5wcTYaoTyRhiPqIiAhCQkLcHY4QTjE+Yjz9l/ZnTOsxhNULc3c4TmHXdkZvHs2Q\nn4ZQq3QtZnecTcWAinetl7hTshAi+zl6FOrXh9KlYf16KFjQ3RHlHFpD5crQvLmZCFt4jwvXLlBz\nXE0CCwWyru86GbAqBTJEvRAiw16o/QKDGwxm0MpBLP1rqbvDybQzV8/QdlZb/rP6PwysN5Bfnv0l\nyQQMkARMiGzs0iV44gkzCfOPP0oC5mpKQYcOsGgRxMe7OxqRHgH5ApjZfiZbjm3h3fXvujucHEuS\nMCFygI+af0To/aF0ndeVXad2uTucDFsXvY6a42qy7fg2lnVfxictPyG3b253hyWEcJGE1js3bpgE\n4NgxMxdY6dJuDiyHat8e/vkHNm1ydyQivRqVa8Q7j7zDyA0jWRe9zt3h5EjpTsKUUv9VSpXPimCE\nEFnD18eX6e2mc3/x+2kzqw0nbCfcHVK6xNvjeWfd/7N373E2V/sfx19rM4jGpVTCxBTVSGKU00Tk\nkksymEmhyymli6Skoh8nCt1IqaiklIxbKUblWsk5wpFbt6FUkxIVFZPrsNfvjzUOaWRmz977u/ee\n9/Px2A97vvu7v9/3lMeMz15rfdYQWk5syZknnsnaW9fSrnY7r2OJSBjk5OTQp89gEhNbkZDQicTE\nVtSrN5jFi3OYNQuSkrxOWHw1agRVq6pLYrS6v8n9NK3RlGvevIZtu7Z5HafYCWQkrCPwtTHmPWNM\nd2NM6WCHEpHgK1eqHLO7zcZaS+qUVHbu2+l1pALZtGMTLSe2ZOjioQxuNpiF1y6kanxVr2OJSBjk\n5OSQkpLOmDEpZGcvYNOmWWRnL2D9+hROOSWdBg1yvI5YrPl8bs+wN990a8QkupTwlWBS2iR279/N\njZk3xmQDr0hW6CLMWlsfuAD4HBgNbDHGPGeMuSDY4UQkuKrGV+Xt7m+zbus6rnnrGg74I3si/ztf\nvsN5z5/HV79+xfvXvc8DzR6ghK+E17FEJEwGDhxJVtbd+P1tgYNrPA3Qlh9/7MugQU94mE7ATUnc\nuBFWrvQ6iQSievnqvJz6MrPWz2LsirFexylWAloTZq1dba3tA1QFbgSqA0uMMZ8YY+40xlQIZkgR\nCZ76Veoz9YqpZK7PZMDCAV7Hyde+A/voN68fl0+5nAurX8jaW9fSrGYzr2OJSJjNnr0Ev79Nvq/5\n/W3JzFwS5kRypKZN4YQTNCUxmnU8uyO9L+hNv/n9+OSnT7yOU2wUtTGHAeKAUnnPfwN6A98bY64q\n4rVFJEQuP/NyRrUexcilIxm3cpzXcf7km9++ocnLTXjmv8/wROsnmN1tNpXLVvY6loiEmbWW3Nxy\nHBoBO5IhN7esplB5rGRJ6NgRZszQlMRoNqL1CM488Uy6vtGVXbm7vI5TLARUhBljGhpjngU2A08C\nq4Eka20za21tYCDwdPBiikiw9flHH3qd34te7/Ri4TcLvY4DwLTPptHghQZs272NJT2WcHfK3Woz\nL1JMGWOIi9sJHO1f9pa4uJ36GREB0tLgyy8hK8vrJBKoMiXLMPWKqWT/nk3fuX29jlMsBNId8VNg\nGZCIm4qYYK0dYK3dcNhpU4CTghNRRELBGMPodqO59IxLuWL6FXzxyxeeZdmVu4ubZ99M1xldaVer\nHatuXsUF1bTMVKS469ChMT7fvHxf8/nmkpraJMyJJD+tWrl92jQlMbrVOakOo9uOZtyqcbz++ete\nx4l5gYyETQdqWmvbW2tnWmv/srLfWrvVWqs9yEQiXElfSaZdMY2ECglcPvlyft75c9gzfPHLFzR6\nsRGvffIa4y4fx5T0KVQoo2WlIgLDh99D9eqjgDkcGhGz+HxzSEp6kmHD+nmYTg4qUwbat1cRFgtu\nSr6JLnW60HN2T777/Tuv48S0QLojDrXWbgpFGBEJv/Kly/N2t7fZlbuLTlM7sWf/nrDc11rLS6te\n4vxx52OxrOi5gp4Ne2pqkYj8T3x8PFWrzqBKleXUrNmaatU6UrNma3r3Xs7SpTOIj4/3OqLkSU+H\n1avh22+9TiJFYYxhXIdxVCxTke5vdme/f7/XkWJWINMRZxhj7s3n+H3GGI1dikShGhVrkNktk9Vb\nVnPDrBtCvtB9x94dXP3m1dw0+ya6n9udFT1XUPfkuiG9p4hEnyVLYNmyeJ5/fgjffruA77+fybff\nLmD06CEqwCJMu3ZQujS89ZbXSaSoKpapyOT0ySz/YTkPffiQ13FiViBTBpsC7+ZzfE7eayIShRpV\na8RrnV9j6mdTGbxocMjus/LHlSS/kMzbX77N5LTJjE8dT9m4siG7n4hEr8ceg6Qk6NDBfa2R8sh1\n/PHQpo2mJMaKixIuYsglQxi2eBiLshd5HScmBVKEHQ/kNzaZC5QvWhwR8dIVda7gkZaPMHTxUCau\nnRjUa1treWrZU6S8lELFMhVZdcsqup3bLaj3EJHY8fnnMHs23Hcf+LTKPCqkpcFHH8HmzV4nkWC4\nv8n9NKvZjGvevIZtu7Z5HSfmBPJj7VMgvz3AugLetVcTkaDo37g/Per34KbMm1j83eKgXHPbrm10\nnNqRvvP6cvsFt7OkxxJqnVArKNcWkdg0YgRUrw7du3udRAqqQwdXMM+c6XUSCYYSvhJM6jyJ3ft3\n0yOzh/bkC7JAirChwL+MMa8aY/6Z95iI2xtsaHDjiUi4GWN47vLnaHJaEzpP68yGXzcc+01/49/f\n/Zv6L9RnyfdLyOyayZNtn6R0ydJBSisisWjjRsjIgL59oVQpr9NIQZ1wAjRvrimJsaRa+WpM6DiB\nzPWZjF0x1us4MSWQ7oizgU5ALWAs8ARQHWhlrdVnHyIxoFSJUsy4cgYnlT2J9pPb8+vuXwt9jQP+\nAwxbPIxLXr2ExIqJrL11LR3O6hCCtCISa558EuLjoWdPr5NIYaWlwQcfwK+F/7UhESr1rFR6X9Cb\nfvP78clPn3gdJ2YENMvaWvuOtbaxtbactbaytbaFtfbDYIcTEe9UOq4Sb3d/m227tpE+PZ19B/YV\n+L2bczbTelJrHvjgAQZePJD3//k+1ctXD2FaEYkVv/4KL74It9/uCjGJLp06gd/v1vNJ7BjRegRn\nnngmXd/oys59O72OExO01FVEjqrWCbV466q3+Oj7j7j17VsLNB987oa5nPf8eXzxyxcsvG4hDzV/\niJK+kmFIKyKxYMwYOHAA7rjD6yQSiFNPhZQUTUmMNWVKlmHaFdPI/j2bvvP6eh0nJgSyT1gJY8w9\nxpj/GmO2GGN+PfwRipAi4p2La1zMS6kvMWHNBB79z6P/O35kQZZ7IJf+C/rTLqMdyacms/bWtbRI\nbBHuuCISxXbtgqefhhtvhJNP9jqNBCotDebNgz/+8DqJBFPSSUk83e5pXlz1Iq9/rq2BiyqQkbDB\nwN3ANKACMAp4E/ADQ4KWTEQixjX1ruGBpg/wf3P+j8tuuozE5EQSGiWQmJxIn/v68Nn3n3HxhIsZ\ntWwUj7d6nHevfpeTy+lfUCJSOC+/DL/9Bv36eZ1EiiItDfbuhTlzvE4iwXZjgxvpUqcLPWf35Lvf\nv/M6TlQzhW03aYz5GuhjrX3HGJMD1LfWfm2M6QNcaK2N2mayxphkYOXKlStJTk72Oo5IRNmxYwcJ\nKQnsaLDDteUxgAXf1z5YCtV7Vmfa1dO4sPqFXkcVkSiUmwu1a8NFF8HkyV6nkaJKToazzoIpU7xO\nIsH2+57fqf98faqVr8aH138Y00sOVq1aRcOGDQEaWmtXBfPagYyEVcHtFQbwB240DOBtoH0wQolI\n5Bk0bBB/JP8BtXEFGO5Pfy0//gv9tPulnQowEQnY9Onw3Xduc2aJfmlp8PbbsGeP10kk2CqWqcjk\n9Mks/2E5Dy560Os4USuQIuwH4NS8518DrfOeXwDsDTSIMeZ2Y8y3xpjdxphlxpgL/ubczsaY+caY\nn40x240xHxljWh9xzj+NMX5jzIG8P/3GmF2B5hMp7mYvnI3/DH/+L9aCeR/MC28gEYkZ1sLjj0Pb\ntlC/vtdpJBjS0tyasPfe8zqJhMJFCRfx4CUPMvzfw1mUvcjrOFEpkCLsLaBl3vNngKHGmK+AicDL\ngYQwxlyF229sMNAAWAvMM8ZUPspbmgLzgXZAMvABMNsYc94R523HjdwdfNQIJJ9IcWetJbdE7qER\nsCMZyPXlFqh7oojIkebOhU8+gf79vU4iwZKU5KYjqkti7BrQZADNajbj6jevZuuurV7HiTqBbNY8\nwFr7cN7zacDFwHPAFdbaAQHm6Au8YK2daK1dB9wK7AJ6HCVDX2vtSGvtSmvt19bagcBXwJE7wVpr\n7S/W2p/zHr8EmE+kWDPGEHcgDo5WY1mIOxCHMUer0kREju6xx6BRI2jWzOskEizGuNGwWbNg/36v\n00golPCVYFLnSezZv4cbM2/UB7GFVKgizBgTZ4x52RiTePCYtXaZtXaUtTagbfmMMXFAQ+B/A9bW\n/V9cCKQU8BoGiAeObJF/vDEm2xiz0Rgz0xhTJ5CMIgIdWnXA903+PzJ8X/tIvTQ1zIlEJBYsWwYf\nfggDBrh/uEvsSEuDbdtg8WKvk0ioVCtfjQkdJ5C5PpOxK8Z6HSeqFKoIs9bmAulBzlAZKAH8dMTx\nn3BTCAviXqAcMP2wY+txI2mpwNW47/UjY0zVIqUVKaaG/2s4SV8l4dvgOzQiZsG3wUfShiSGDRrm\naT4RiU6PPeamrXXs6HUSCbaGDSEhQVMSY13qWan0vqA3/eb3Y+2WtV7HiRqBtKh/FVhjrX0yKAGM\nORXYBKRYa5cfdvwxoKm19m9Hw4wx3YEXgFRr7Qd/c15JIAuYbK0dfJRzkoGVTZs2pUKFCn96rVu3\nbnTr1q2A35VIbMrJyWHQsEFkLswk15dLnD+O1FapDBs0jPj4eK/jiUiUycqCOnVg/Hi3QbPEnrvu\ngtdfh++/B18gnQgkKuzZv4d/jP8H+w7s4+OeH1OuVDmvIxXalClTmHLEngrbt29nsRvKDXqL+kCK\nsEFAP9z0wZXAzsNft9Y+XcjrxeHWf6VbazMPO/4KUMFa2/lv3tsVGI9bjza3APeaDuRaa68+yuva\nJ0ykgKy1WgMmIkXSowfMmwfffAOlS3udRkJh8WK31m/pUrhQu5jEtKxfsmg4riHX1LuGcR3GeR0n\nKCJtn7Abgd9x67huxjXVOPi4q7AXy5viuJJDHRcPrvFqCXx0tPcZY7oBLwFdC1iA+YBzgc2FzSgi\nf6UCTESK4ocfYNIk6NtXBVgsa9wYTj5ZUxKLg6STkni63dO8uOpFXv/8da/jRLxAuiMm/s3j9ABz\njAJ6GmOuM8acDTwPlAVeATDGPJI3DZK8r7sDr+JG5FYYY07Je5Q/7Jx/GWMuNcYkGmMaABnAabiR\nMxEREfHQU09B2bJw881eJ5FQKlECOnVyRZia58W+GxvcSJc6Xeg5uyfZv2d7HSeiRcTsXGvtdOAe\n4CFgNVAPaHNYS/kqQMJhb+mJa+YxBvjxsMdTh51TCRgHfAG8AxyPW3e2LnTfiYiIiBzLb7/BCy9A\nr15Qvvyxz5folpYGX38Nn37qdRIJNWMM4zqMo2KZinSf0Z39fu1PcDQlC/sGY8zfbshsrc13b69j\nsdaOBfLtbWmtveGIr5sX4Hp3A3cHkkVERERCZ+xYyM2FO+/0OomEQ/PmUKGCGw2rV8/rNBJqFctU\nZHL6ZJpOaMqDix5kaIuhXkeKSIGMhFU64nEy0AJIAyoGL5qIiIjEmt27YfRouOEGOOUUr9NIOJQq\nBR06aF1YcXJRwkU8eMmDDP/3cBZlL/I6TkQKZE1Y5yMelwOnA9OAZUFPKCIiIjHjlVfcBr733ON1\nEgmntDQ3HfGrr7xOIuEyoMkAmtVsxtVvXs3WXVu9jhNxgrImzFrrxzXX6BuM64mIiEjs2b8fRoyA\nLl3gjDO8TiPh1KYNHHecRsOKkxK+EkzqPIm9+/fSY1YPCrstVqwLZmOOMwhgjZmIiIgUD2+8Ad9+\nC/37e51Ewq1sWWjXTkVYcVOtfDUmdJzA7C9nM2bFGK/jRJRAGnOMOvIQcCrQHtc2XkRERORPrIXH\nHoNLL4UGDbxOI15IS4NrroHvv4eEhGOfL7Ghw1kduKPRHdwz/x4uPu1izqtynteRIkIgI2ENjngc\n7HPTjwA2axYREZHYN38+rFkDAwZ4nUS80r49xMXBzJleJ5Fwe/zSxzmr8ll0ndGVnft2eh0nIgTS\nmKP5EY+W1tqu1tpx1lptBiAiIiJ/8dhjcP75rl25FE8VK0KrVpqSWByVKVmGqelT2bh9I33nHWoh\nUZzXiRW6CDPGJBpjaudzvLYxpmYwQomIiEjsWLECPvjArQUzxus04qW0NFi8GH75xeskEm5JJyXx\ndNuneXHpi1x202UkJieS0CiBxORE+tzXh5ycHK8jhlUg0xFfAf6Rz/F/5L0mIiIi8j+PPQa1a0Pn\nzl4nEa+lpro/MzO9zSHe6FKrC+VnlWfOnjlkp2az6fJNZKdmM2bLGFJapxSrQizQNWFL8zm+DKhf\ntDgiIiISS9avd9PP7r0XSpTwOo147eST4eKLNSWxuBo0bBB/JP8BtXGt/XB/+s/wk1Uri0HDBnkZ\nL6wCKcIsUD6f4xUA/XgVERGR/xk5Ek45Ba691uskEinS0mDhQti+3eskEm6zF87Gf4Y/39f8Z/jJ\nXFh8hkgDKcIWA/cbY/5XcOU9vx/4T7CCiYiISHT78UeYOBHuugvKlPE6jUSKzp1h3z545x2vk0g4\nWWvJLZF7aATsSAb2+fYVm2YdgRRh/YEWwHpjzARjzARgPdAUuDeY4URERCR6PfWUK75uvdXrJBJJ\nEhLgggs0JbG4McYQdyDOzanLj4Utv23h0f88yu97fg9rNi8E0qL+C9zeYNOBk4F4YCJwtrX2s+DG\nExERkWj0++/w/PNw221QoYLXaSTSpKXBnDmwa5fXSSScOrTqgO+b/MsP39c+6iTXYciHQzjtydPo\nv6A/m3M2hzlh+AQyEoa19kdr7f9Za9tba6+w1j5krf012OFEREQkOj3/POzdC3fe6XUSiURpaa4A\nmz/f6yQSTsP/NZykr5LwbfAdGhGz4NvgI2lDEh+99BHZd2bT64JePPfxc9QcXZNbZt/Chl83eJo7\nFALZJ+wGY0yXfI53Mcb8MzixREREJFrt2eOmIl5/PZx6qtdpJBKdeSacc46mJBY38fHxLJ2/lN5V\ne1Nzdk2qvV2NmrNr0rtqb5bOX0p8fDynxp/Ko60eZWPfjTx4yYPMXD+Ts549i65vdGX15tVefwtB\nYwq7+M0Y8yVwk7V28RHHmwHjrLVnBTFfWBljkoGVK1euJDk52es4IiIiUemFF9w0xPXr3f5gIvkZ\nPBiefhp++glKlfI6jXjBWos5xg7uu3N388qaVxjx0Qi+/f1b2pzRhgFNBtCsRrNjvreoVq1aRcOG\nDQEaWmtXBfPagUxHPA3YmM/x7/JeExERkWLqwAHXlj49XQWY/L20NLd2cNEir5OIVwpSRB0Xdxy3\nXXAbX97xJZPTJrP5j800f7U5KS+lMHPdTPw2/5b3kS6QIuxnXGOOI50HbCtaHBEREYlmb74JGzZA\n//5eJ5FIV68enH66piRKwZT0laTbud1Yc8sa3un+DqVKlKLztM7UHVuXV9e8yr4D+7yOWCiBFGFT\ngKeNMc2NMSXyHi2A0cDU4MYTERGRaGEtPPootGwJ55/vdRqJdMa40bCZM90IqkhBGGO4rPZlLL5h\nMf+54T/UOqEW18+6nlpP12L0stHs3LfT64gFEkgR9i9gOfAesDvvMR94H/i/4EUTERGRaPLee7Bq\nlUbBpODS0tyasKVLvU4i0ajxaY3J7JbJp7d9SrOazeg3vx81nqrBg4seZNuuyJ6gF8g+YfustVcB\nZwNXA2nAGdbaHtba6BoHFBERkaB57DFo0ABatfI6iUSLf/zDddCcMcPrJBLN6p5cl9c6v8aGPhvo\nVrcbjy55lBpP1eDueXfzw44fvI6Xr4D2CQOw1n5prX3dWvu2tfa7YIYSERGR6LJyJSxcCAMGuGlm\nIgXh80Hnzm5dWCEbdov8Rc2KNXnmsmf47q7v6HthXyasmcDpo0+nx6werNu6zut4fxJQEWaMqW6M\n6WWMedQYM+rwR7ADioiISOR77DE44wzXFVGkMNLSYONGN5VVJBhOLncyQ1sMZeNdG3mk5SPM+3oe\ndcbUIX16Ois2rfA6HhDYZs0tgfXAbUA/oDlwA9ADqB/UdCIiIhLxNmxw08nuuQdKlPA6jUSbpk3h\nhBPUJVGCL750PP0u6sc3fb5hXIdxfPrTpzQa34iWE1uy4OsFFHa/5GAKZCTsEWCktfZcYA+QDiQA\nHwKvBzGbiIhITPDyF304jBwJJ50E11/vdRKJRnFxkJqqIkxCp3TJ0tyUfBNZt2fxepfX+X3P77Se\n1JrzXzyf1z9/nQP+8LfnDKQISwIm5j3fDxxnrf0DeABQPyQREREgJyeHPn0Gk5jYioSETiQmtqJP\nn8Hk5OR4HS2otmyBV16BO++EMmW8TiPRKj0d1q2DrCyvk0gsK+ErwRV1ruDjnh+z4NoFVCpTiSvf\nuJKkMUmMXzWevfv3Ank/v+/rw+XdLw9ZlkCKsJ1Aqbznm4EzDnutcpETiYiIRLmcnBxSUtIZMyaF\n7OwFbNo0i+zsBYwZk0JKSnpMFWKjR0OpUnDbbV4nkWjWqhUcf/zBBh2xPXIs3jPG0Or0Viy8biHL\nb1rOuaecy82zbyZxdCLDFgzjH5f+gzGbx7C52eaQZQikCFsGNMl7/i7whDFmIPBy3msiIiLF2sCB\nI8nKuhu/vy1wsFWgwe9vS1ZWXwYNesLLeEGzfTuMHQu33goVK3qdRqJZbm4Op546mIceiu2RY4k8\njao1YsaVM/ji9i9oV6sdDwx/gKxaWfhr+UN630CKsLtxmzUDDMZt2nwVkA3cGJxYIiIi0Wv27CX4\n/W3yfc3vb0tm5pIwJwqNF16APXvgrru8TiLR7ODI8YYNKezbF9sjxxK5zq58Ni91fIlqv1eDWqG/\nXyCbNX9jrf0k7/lOa+2t1tp61tp07RcmIiLFnbWW3NxyHBoBO5IhN7ds1E+52rsXnnoKrr0Wqlb1\nOo1Es4Mjx9bG9sixRD5rLTbOHv3HdxAFvFmziIiI5MewZ89O4GhFlgV2YqJ8R+PXXnNNOe691+sk\nEu2Ky8ixRD5jDHEH4o7+4zuIVISJiIgEydatrsvbtm2NgXlHOWsuW7Y0YcIEiNbBsAMHYMQI6NwZ\nzjrL6zQSzYrLyLFEjw6tOuD7JvQlkoowERGRIJg3D849FxYvhoyMezjnnFH4fHM49JGqxeebQ1LS\nk3Tv3o8ePdxUvmhc7jJzJnz5JfTXxjRSRMYY4uL+fuQ4Li76R44legz/13CSvkrCtyG0ZZKKMBER\nkSLYvRv69IG2beG88+DTT6F793iWLp1B797LqVmzNdWqdaRmzdb07r2c5ctnMHFiPBkZMGsWNGwI\nq1d7/V0UnLXw2GNwySXQqJHXaSQWdOjQGJ8v/5Fjn28uqalN8n1NJBTi4+NZOn8pvav25tTFp4bs\nPkbDu4cYY5KBlStXriQ5OdnrOCIiEuHWrIGrr4ZvvoHHH4fevSG/D+yttfl+kv/VV9C1K3z2GYwc\nefT3R5IPPoAWLWDuXGiT/zIekUI52B0xK6vvYds6WHy+uSQlPcnSpTOIj4/3OqYUQ6tWraJhQete\nbwAAIABJREFUw4YADa21q4J57ZIFOckYM6qgF7TW3h14HBERkcjn98MTT8DAgVCnDnz8MZxzztHP\nP9pUqtq14aOP4L773Gjae+/Byy/DCSeEKHgQPPoo1K8PrVt7nURiRXy8GzkeNOgJMjNHkZtblm3b\ndlGpUmMVYBKzClSEAQ0KeJ6G1UREJKZ9/z1cdx18+KHrDPjQQ1C6dODXK10aRo+Gli3h+utdgTNl\nCjRuHLTIQbN6NcyfD5MnR/6InUSX+Ph4Ro8ewujRbuT4rbcM6elulPm887xOJxJ8BSrCrLXNQx1E\nREQk0k2dCrfeCuXLw/vvu3VRwZKaCmvXQrdu0KyZK+4GDABfBK3efvxxSEyELl28TiKxzBhDaqrb\nf27sWLcpuEisiaAf7SIiIpHp99/hmmtcgdSunSuWglmAHZSQAIsWueJr0CC35mrLluDfJxDffAPT\np8M990DJgs6jEQlQyZJwyy0waRJs3+51GpHgC6gIM8acb4x53Bgz1Rjz5uGPYAcUERHx0ocfuulQ\ns2dDRoabKlipUujuV7IkDBvmpv19+qm794IFobtfQY0cCSeeCDfc4HUSKS5uugn27YOJE71OIhJ8\nhS7CjDFdgY+AJKAzEAecA7QA9FmFiIjEhH374P77oXlzqFkTPvkEuncP3/1btXIjbvXruxGx//s/\nyM0N3/0P9/PPMGGCax5y3HHeZJDip2pVtyH42LHRu7G5yNEEMhL2f0Bfa20HYB9wJ3A2MB3YGMRs\nIiIinsjKggsvdB0QH3nErf+qUSP8OU45BebMcRkef9xNgfzuu/DnePppN0J3++3hv7cUb716wbp1\nbpquSCwJpAg7A3gn7/k+oJx1m409CdwcrGAiIiLhZi2MGQPJyW4T5mXLoH9/KFHCu0w+n8vw73/D\nDz+4kbGZM8N3/5wc99/k5ptDOw1TJD/NmkFSkhsNE4klgRRhvwEHN2zYBNTNe14RKBuMUCIiIuG2\nZQu0b+82TL7xRli50hVjkSIlxW0O3by5m6J1xx2wZ0/o7ztuHOzcCX37hv5eIkcyxo2GvfUW/Pij\n12lEgieQImwxcGne89eB0caYF4EpwHvBCiYiIhIus2bBuefCqlXwzjvw7LNQNgI/VqxUCWbMcPnG\njXOF2Zdfhu5+e/fCqFGuM2T16qG7j8jfufZaKFMGXnzR6yQiwRNIEdYbmJr3fDgwCjgFmAHcGKRc\nIiIiIbdzp5tm16kTXHSR60Z42WVep/p7xri1WcuXw65dbrRu0qTQ3Csjw40+3HtvaK4vUhAVKrgP\nAsaN8645jUiwFboIs9b+aq39Me+531r7qLU21Vrbz1r7W/AjioiIBN9//wsNGrhCY9w4t87qpJO8\nTlVw9eu7KZNpaW6k4Prr4Y8/gnd9v981A+nY0a3JEfHSbbe5DwQyM71OIhIcgbSoX2iMud4YUz4U\ngUREREJp/34YOtSNfFWs6NZZ9ezpRpiizfHHuz2UXn0V3ngDzj/ftdIPhsxMWL/ebRwt4rXzzoPG\njdWgQ2JHINMRPwceAbYYY143xnQ0xsQFOZeIiEjQffMNNG0KQ4a4fbeWLIHatb1OVXTXXedGxUqX\nhkaN4LnniravkrXw6KPuv9WFFwYvp0hR9OrltovIyvI6iUjRBTId8U6gGtAJ2AlMBH4yxowzxjQL\ncj4REZEisxZeecV9mr5li2v3/tBDEBdDHyGedZZbJ3bjje4fq126wO+/B3atxYvdtfr3D25GkaJI\nT3dThp9/3uskIkUXyEjYwbVg86211+OactwCNALeD2I2ERGRItu2zRUkN9zg/lyzxk1FjEVlyrg9\nvd54AxYudGveli8v/HUee8x1i2zXLvgZRQJVujTcdJP7QGXnTq/TiBRNQEXYQcaYKsCtQH+gHrAi\nGKFERESCYcECqFcPPvgAXn8dXn4ZyheDFc3p6a7YrFIFmjSBESNco42C+OQTmDPHjYJF4zo5iW23\n3OI2EJ882eskIkUTSGOO8saYG4wxC4DvgduATKC2tVYzx0VEJOzsEQug9uxxmwu3bg116rjC4oor\nPArnkZo13bTCfv3gvvvcRtQ//3zs9z32GNSoAVddFfKIIoVWowZcfrlr0FGUdY8iXgtkJOwn3P5g\nnwEp1tqzrLUPWWu/Dm40ERGRo8vJyaFPn8EkJrYiIaETiYmt6NNnMEuX5nD++a45xVNPwbx5UK2a\n12m9ERfnGmzMnesad9Sv7xobHE12Nkyb5gq3kiXDFlOkUHr1ciO9y5Z5nUQkcIEUYalAdWttX2vt\nx8EOJCIiciw5OTmkpKQzZkwK2dkL2LRpFtnZC3j22RQuuigda3NYsQLuvBN8RZp4HxvatIG1a92o\nYKtW8MADrlX/4ay1PPEEVKrkmnuIRKrWreH009WuXqJbIN0RF1hrCzizXEREJPgGDhxJVtbd+P1t\ngYMLlwzWtsWYvjRv/gTnnutlwshz6qluVHDoUBg+HFq0gHXrDo0mVq3aiTFjWnHGGYM5cCDH67gi\nR+Xzuc2bp0+HX37xOo1IYCLm80FjzO3GmG+NMbuNMcuMMRf8zbmdjTHzjTE/G2O2G2M+Msa0zue8\nLsaYrLxrrjXGqM+TiEgMmD17CX5/m3xfs7Yt77yzJMyJokOJEjBwIHz4IXz9dQ7nnJPOs8+60cQt\nW2Zh7QJWrEghJSWdnBwVYhK5brjBNY55+WWvk4gEJiKKMGPMVcATwGCgAbAWmGeMqXyUtzQF5gPt\ngGTgA2C2Mea8w655ETAZeBGoD8wCZhpj6oTq+xARkdCz1pKbW45DI2BHMuTmlv1Lsw45pEkTaN9+\nJH7/3Vj759FEv78tWVl9GTToCS8jivytE0+Erl3dnmEHDnidRqTwIqIIA/oCL1hrJ1pr1+Ha3u8C\neuR3ct56tJHW2pXW2q+ttQOBr4AOh53WB5hjrR1lrV1vrX0AWAX0Du23IiIioWSMIS5uJ3C0IssS\nF7cTo/7qf2vBgiVA/qOJfn9bMjM1miiRrVcv10xm7lyvk4gUXiAt6q8zxpTO53gpY8x1AVwvDmgI\nvHfwmHUfXy4EUgp4DQPEA78edjgl7xqHm1fQa4qISOTq0KExPt+8fF/z+eaSmtokzImii0YTJRZc\ncAE0bKgGHRKdAhkJmwBUyOd4fN5rhVUZKIFrfX+4n4AqBbzGvUA5YPphx6oU8ZoiIhKhhg+/hxo1\nRgFzODQiZvH55pCU9CTDhvXzMF3k02iixAJj3GjYnDnwzTdepxEpnECKMEP+P7WrA9uLFqfwjDHd\ngX8BXay1W8N9fxERCb/4+HhatZpB2bLLqVmzNdWqdaRmzdb07r2cpUtnEB8f73XEiKfRRIkFXbtC\nhQrwwgteJxEpnAJvxWiMWY0rvizwnjHm8B1GSgCJQCCzcrcCB4BTjjh+CrDlGJm6AuOAK6y1Hxzx\n8pZArgnQt29fKlT482Bft27d6Nat27HeKiIiYZCbC2+9Fc9ttw1h5Eg3vU6jNoUzfPg9vP9+OllZ\n9rBW/xafb27eaOIMryOKHFPZsq5T4ksvwYMPQpkyXieSaDVlyhSmTJnyp2Pbt4dufMkUdL63MWZw\n3tPBuE6Gfxz28j4gG5hhrd1X6BDGLAOWW2vvzPvaABuBp621I47ynm7AeOAqa+3b+bw+FTjOWtvx\nsGNLgLXW2l5HuWYysHLlypUkJycX9tsQEZEwefddaN8eVq2CBg28ThO9cnJyGDToCTIzl5CbW5a4\nuF2kpjZm2LB+Gk2UqPHVV3DmmTBxIlx7rddpJJasWrWKhg0bAjS01q4K5rULXIT97w3G/BOYaq3d\nG7QQxlwJvILrivhfXLfEK4CzrbW/GGMeAapaa/+Zd373vPP7AG8ddqnd1todeeekAIuA+4F3gG7A\nACDZWvvFUXKoCBMRiQLXXOMKsM8/d+tCpOg0mijRrE0b2LEDli71OonEklAWYYGsCXsfOOngF8aY\nRsaYp4wxNwcawlo7HbgHeAhYDdQD2lhrD+6DXgVIOOwtPXFTIMcAPx72eOqway4FugM3A2uANKDj\n0QowERGJDjt3wsyZ0L27CrBgUgEm0axXL1i2zH04IxINCrwm7DCTceuwXjPGVMG1gf8MuNoYU8Va\n+1AgQay1Y4F8m4xaa2844uvmBbzmDECT2kVEYsisWa4Q697d6yQiEinat4eEBNeufvx4r9OIHFsg\nI2F1cVMGAa4EPrXWXgRcDVwfpFwiIiL5ysiAlBQ4/XSvk4hIpChZEm65BSZPht9+8zqNyLEFUoTF\nAQfXg7UCMvOerwNODUYoERGR/PzyC8ybB1df7XUSEYk0N94I+/fDq696nUTk2AIpwj4HbjXGXAxc\nyqG29FWBbcEKJiIicqTp092fV17pbQ4RiTxVqkB6upuS6Pd7nUbk7wVShPUHbsF1HpxirV2bdzyV\nQ9MURUREgi4jw3VBO+mkY58rIsVPr16uZf3773udROTvFboxh7V2kTGmMlDeWnv4rNtxwK6gJRMR\nETnMN9+49tMZGV4nEZFI1aQJ1K3rRsNatfI6jcjRBTISBmCAhsaYW4wxB3dz3IeKMBERCZHJk6Fc\nOejY0eskIhKpjHGjYbNmwQ8/eJ1G5OgKXYQZY2oAnwKzcPt0HZwU0h8YGbxoIiIijrVuBKxTJ1eI\niYgczTXXQNmyMG6c10lEji6QkbDRwMdAJWD3YcffAloGI5SIiMjh1qyBdevUFVFEji0+Hq67Dl58\nEfbt8zqNSP4CKcIuBoZZa4/8a50NVCtyIhERkSNkZEDlylrjISIFc9ttsGULzJzpdRKR/AVShPmA\nEvkcrw7kFC2OiIjInx04AFOmwFVXQVyc12lEJBrUrQtNm7oGHSKRKJAibD5w12FfW2PM8cCDwLtB\nSSUiIpLnww/hxx81FVFECqdXL/fz4/PPvU4i8leBFGH9gMbGmC+AMsBkDk1F7B+8aCIiIm4q4umn\nw4UXep1ERKJJ585wyinw3HNeJxH5q0IXYdbaH4DzgOHAk8BqYADQwFr7c3DjiYhIcbZnD7zxBnTv\n7lpPi4gUVKlS0LMnTJwIOVowIxEmoH3CrLX7rbUZ1tr7rLW9rLXjrbW7j/1OERGRgnvnHdixQ1MR\nRSQwN98MO3dqk3eJPIHsE3biYc8TjDEPGWNGGGOaBjeaiIgUdxkZkJwMZ5/tdRIRiUYJCZCa6hp0\nWOt1GpFDClyEGWPONcZkAz8bY9YZY+oDK4C+wC3A+8aYTqGJKSIixc1vv7mRMI2CiUhR9OoFn34K\nS5Z4nUTkkMKMhD0OfAo0BRYBbwPvABWAisALuLVhIiIiRfbmm5CbC127ep1ERKJZy5ZQu7ba1Utk\nKUwRdgEw0Fq7BLgHqAqMtdb6rbV+4BlAE0ZERCQoMjKgRQuoWtXrJCISzXw+t3nzG2/ATz95nUbE\nKUwRdgKwBcBa+wewE/jtsNd/A+KDF01ERIqrTZtg0SLXFVFEpKiuvx5KloSXXvI6iYhT2MYcRy5p\n1BJHEREJuilTXHvp9HSvk4hILKhUCbp1g+efhwMHvE4jAiULef4rxpi9ec/LAM8bY3bmfV06eLFE\nRKQ4y8iAyy+HChW8TiIisaJXL3j5ZdfwJzXV6zRS3BVmJOxV4Gdge95jEvDjYV//DEwMdkARESle\nvvgC1qxRV0QRCa6GDaFRIzXokMhQ4JEwa+0NoQwiIiICbhSsYkW47DKvk4hIrOnVy60P27ABatXy\nOo0UZ4XerFlERCRUrIXJk+GKK6C0JrmLSJBdeSWccIJbGybiJRVhIiISMT76CLKzNRVRRELjuOOg\nRw+3Nmz3bq/TSHGmIkxERCLG5MlQvTo0bep1EhGJVbfeCr//DtOmeZ1ErC2+jdZVhImISETIzYXp\n010baZ9+O4lIiJxxBrRtqwYdXsnJyaFPn8EkJrYiIaETiYmt6NNnMDk5OV5HCyv9mhMRkYgwfz5s\n3aqpiCISer16wYoV7iHhk5OTQ0pKOmPGpJCdvYBNm2aRnb2AMWNSSElJL1aFmIowERGJCBkZUKcO\n1KvndRIRiXXt2kGNGhoNC7eBA0eSlXU3fn9bwOQdNfj9bcnK6sugQU94GS+sVISJiIjn/vgDZs1y\no2DGHPt8EZGiKFHCrQ2bOhW2bfM6TfExe/YS/P42+b7m97clM3NJmBN5R0WYiIh4buZM2LULunf3\nOomIFBc9eoDfD6+84nWS4sFaS25uOQ6NgB3JkJtbttg061ARJiIinsvIgMaNoWZNr5OISHFx8snQ\npQs895wrxiS0jDHExe0EjlZkWbZs2cmDDxq+/jqcybyhIkxERDz188+wYIEacohI+PXqBV9/7X4G\nSeh16NAYY+bl+5rPN5ezzmrCqFFQqxZcfDG8+KLbTiAWqQgTERFPTZvm1oF16eJ1EhEpblJS4Lzz\n1KAjXK666h6sHYUxczg0Imbx+eaQlPQky5b1Y8sWNzuiXDm3bq9KFejaFd59F/bv9zJ9cKkIExER\nT02e7PbsqVzZ6yQiUtwY40bD3n4bvvvO6zSxze+He++N56yzZnD77cupWbM11ap1pGbN1vTuvZyl\nS2cQHx9P2bJuffDcufD99zB0KHz2GbRvD9WrQ79+sHat199N0ZnisvitIIwxycDKlStXkpyc7HUc\nEZGY9/XXbtrJlCnuk04RkXD74w+oVg1694bhw71OE7teegluugkWLYJmzdwxay2mAC1xrYXVq2Hi\nRPfB3S+/uBHM665zBVuVKqHJvGrVKho2bAjQ0Fq7KpjX1kiYiIh4ZvJkOP54SE31OomIFFfHHw//\n/CeMHw9793qdJjZt3Qr33QfXXnuoAAMKVIC58yA5GZ56CjZtgsxM9wHe/fe70bH27d3U9j17QvQN\nhICKMBER8YS1bt5/585QtqzXaUSkOLvtNtck6M03vU4Sm+6/Hw4cgBEjin6tuDjo0AHeeAM2b4Zn\nn4Vff3WzKapUgZtvhiVL3O+YSKYiTEREPLFqFaxfr73BRMR7SUnQvLkadITC0qVulPHhh+GUU4J7\n7RNOcM07li51v09694Z586BJE6hdGx58EL79Nrj3DBYVYSIi4omMDLdPT6tWXicREXENOv7zH/jk\nE6+TxI79+91/14YN4ZZbQnuvM8+EYcNc0fX++67F/ciRcPrp0LSpW5O2fXtoMxSGijAREQm7Awdg\n6lS46iooWdLrNCIi0LEjnHqq27xZgmPsWNfJ8LnnoESJ8NzT53OjmhMmwJYt8NprUKYM9Ozppise\n7Lzodbt7FWEiIhJ2H3zg5vJrg2YRiRRxcW490WuvwY4dXqeJfps3w6BBbgTsggu8yVCuHFxzDcyf\nDxs3wpAhsGYNtGsHp50G994Ln3761/fl5OTQp89gLr/81pBlUxEmIiJhl5EBZ5wBjRp5nURE5JCe\nPV2Hvdde8zpJ9OvXz41APfyw10mc6tWhf3/4/HNYsQLS091oWb16hzov/vyzK8BSUtIZMyaFzZtD\nNyyqIkxERMJq926YMcONghWwO7GISFhUqwadOrlpdJHeXS+Svfee2/9xxAioVMnrNH9mDJx/Pjzz\nDPz4I8ycCTVruhb6VatCvXoj+eKLu/H72wKh+yWlIkxERMLqnXcgJ0dTEUUkMvXqBV98AYsXe50k\nOu3dC7ff7hpjXHed12n+XqlSbi3gm2+66ZNPPw0//rgEa9uE/N4qwkREJKwyMtynkGee6XUSEZG/\nat4czjpL7eoDNWoUbNjg/vtF02yHE0+E226znHRSOUI5AnaQijAREQmb336Dd9/VKJiIRC5j3GjY\nwdERKbjsbBg6FO66C+rW9TpN4RljiIvbCYR+LqqKMBERCZs33nBtga+6yuskIiJHd911bqra+PFe\nJ4kud97pNlAePNjrJIHr0KExPt+8kN9HRZiIiIRNRga0aOH24hERiVQVK7oR+xde8H4/qWiRmeke\nTz0F8fFepwnc8OH3kJQ0Cp9vDqEcEVMRJiIiYfH99/Dhh5qKKCLRoVcv2LQJZs/2Oknk27UL+vSB\nNm1c6/doFh8fz9KlM+jdezmnntorZPdRESYiImExZYrbMyYtzeskIiLHVr8+pKSoQUdBDB8OW7bA\ns89GVzOOo4mPj2f06CG8/bb2CRMRkSiXkQEdOkD58l4nEREpmF69YOFCWL/e6ySRa/16tx/YgAFQ\nq5bXaaKHijAREQm5zz6DTz7RVEQRiS5XXAGVK8Pzz3udJDJZ6/YES0iA/v29ThNdVISJiEjITZ4M\nlSpBu3ZeJxERKbgyZeDGG2HCBNi50+s0kWfaNHjvPTcN8bjjvE4TXVSEiYhISPn9rgjr0sW1fBYR\niSa33AI7dsDUqV4niSzbt0Pfvm6drz5gKzwVYSIiElIffQTffaepiCISnRIToX17GDPGTb8TZ/Bg\nyMlxLeml8FSEiYhISGVkuPUCTZp4nUREJDC9esHq1fDf/3qdJDKsWQPPPOMKsYQEr9NEp4gpwowx\ntxtjvjXG7DbGLDPGXPA351YxxmQYY9YbYw4YY0blc84/jTH+vNf9eY9dof0uRETkcPv2wfTp0L07\n+CLmN46ISOG0aeNGxMaM8TqJ9/x+V5SefTbcdZfXaaJXRPxKNMZcBTwBDAYaAGuBecaYykd5S2ng\nZ2AosOZvLr0dqHLYo0awMouIyLHNmwe//uqKMBGRaOXzwW23uUYUW7eCLcbzEidMgKVL3f5pcXFe\np4leEVGEAX2BF6y1E62164BbgV1Aj/xOttZ+Z63ta62dBOz4m+taa+0v1tqf8x6/BD+6iIgcTUYG\n1K0L9ep5nUREpGi6dMlh//7BnHlmKxISOpGY2Io+fQaTk5PjdbSw2boV7rsPrr0WmjXzOk1087wI\nM8bEAQ2B9w4es+7jhYVAShEvf7wxJtsYs9EYM9MYU6eI1xMRkQLKyYHMTDXkEJHol5OTw+WXp+P3\np/DbbwvYtGkW2dkLGDMmhZSU9GJTiN1/Pxw44DZnlqLxvAgDKgMlgJ+OOP4TbgphoNbjRtJSgatx\n3+tHxpiqRbimiIgU0Ftvwe7d0K2b10lERIpm4MCRZGXdDbQFTN5Rg9/flqysvgwa9ISH6cJj6VIY\nPx4efhhOOcXrNNEvEoqwkLDWLrPWTrLWfmKt/TeQBvwC3OJxNBGRYmHyZLj4Yqih1bgiEuVmz16C\n398m39f8/rZkZi4Jc6Lw2r/fNeNo2NDtmyZFV9LrAMBW4ABwZE19CrAlWDex1u43xqwGah3r3L59\n+1KhQoU/HevWrRvd9HGuiEiB/PQTLFjgFm6LiEQzay25ueU4NAJ2JENublmstRhztHOi29ixsHYt\nLF8OJUp4nSY0pkyZwpQpU/50bPv27SG7n+dFmLU21xizEmgJZAIY9ze4JfB0sO5jjPEB5wLvHOvc\nJ598kuTk5GDdWkSk2Jk2zf2i7tLF6yQiIkVjjCEubidgyb8Qs8TF7YzZAmzzZhg0yI2AXXDUDaSi\nX34DLqtWraJhw4YhuV+kTEccBfQ0xlxnjDkbeB4oC7wCYIx5xBjz6uFvMMacZ4ypDxwPnJT3ddJh\nr//LGHOpMSbRGNMAyABOA8aH51sSESm+MjKgXTs44QSvk4iIFF2HDo3x+eYd5dW51KkTu7vR9+sH\nZcq4tWASPJ6PhAFYa6fn7Qn2EG4a4hqgzWEt5asAR+7HvRr3kQRAMtAd+A44Pe9YJWBc3nt/A1YC\nKXkt8EVEJES++gr++183GiYiEguGD7+H999PJyvL4vcfbM5h8fnmcvzxT/LuuzN4+GHXPTCWBsTe\new+mTIFXXoFKlbxOE1sioggDsNaOBfJdPWCtvSGfY387imetvRu4OzjpRESkoCZPhuOPhw4dvE4i\nIhIc8fHxLF06g0GDniAzcxS5uWWJi9tFampjhg6dwahR8QwcCFlZ8OKLbuQo2u3dC7ff7hosXXed\n12liT8QUYSIiEv2sdVMR09LguOO8TiMiEjzx8fGMHj2E0aP5SxOOIUPg7LPhhhvgm2/cFh0nn+xd\n1mAYNQo2bIA33oit0b1IESlrwkREJAZ8/LGbjqgNmkUkluXXhKNrV1i0yBVhjRrBp5+GP1ewZGfD\n0KFw111Qt67XaWKTijAREQmajAy3iWeLFl4nEREJv3/8w62JrVQJLroI3n7b60SBufNO11hp8GCv\nk8QuFWEiIhIUBw7A1Knu0+CSmuwuIsVUQgL8+9/QqhWkprppfdYe+32RIjPTPZ56CuLjvU4Tu1SE\niYhIULz/vtukWVMRRaS4O/54mDED+vd3Ld5vvhn27fM61bHt2gV9+kCbNpCe7nWa2KbPKkVEJCgy\nMqB2bTj/fK+TiIh4z+eDRx5xDTt69nTrZWfMgBNP9DrZ0Q0fDlu2wMKFasYRahoJExGRItu9G958\n042C6Re3iMgh//ynmynw+eduzdi6CN2xdv16GDHCjd7VquV1mtinIkxERIps9mzIydFURBGR/DRp\n4hp2lCkDF14I8+d7nejPrHV7giUkwIABXqcpHlSEiYhIkWVkuJbM+vRURCR/iYnw0UfQuDFcdhmM\nGeN1okOmTYP33oNnn9Uej+GiIkxERIrk119hzhyNgomIHEv58q7z4B13QO/e7rF/v7eZtm+Hvn0h\nLQ3atfM2S3GixhwiIlIkr7/u2tNfeaXXSUREIl+JEvDkk5CU5KYAfvklTJ8OFSt6k2fwYDed/Kmn\nvLl/caWRMBERKZKMDLcfTpUqXicREYkeN9/s1oZ9/DGkpMCGDeHPsGYNPPOMK8QSEsJ//+JMRZiI\niARs40a3KammIoqIFF7z5rB8Ofj9rnPiokXhu7ffD716uRb6d90VvvuKoyJMREQCNmWKW8TdubPX\nSUREolPt2rBsGTRoAJdeCi+9FJ77TpgAS5fC2LEQFxeee8ohKsJERCRgGRmQmgrx8V4nERGJXpUq\nuQZHPXvCTTfBPfe4tbahsnUr3HcfXHstNGsWuvvI0akIExGRgHz6qXtoKqKISNHFxbnPcho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LIkLIylS5+kXUl/houD1NRUMjLGkpFBqceyx4vFGBvJHmOyxwcWY6yUhxhN+RTPJL8sfkBQVaZN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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd66a9f198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(10, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Question:\n",
    "Describe the results of this experiment, and try to give a reason why the experiment gave the results that it did."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Answer:\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
